Using a Complex Systems Approach to Understanding Population Mental Health.

使用复杂的系统方法来了解人群心理健康。

基本信息

批准号：
MR/N014588/1
负责人：
Daniel Lewis
金额：
$ 35.05万
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FN014588%2F1
关键词：
Using Complex Systems Approach Understanding

项目摘要

There is a growing consensus that it is not just individual characteristics and behaviours that influence health and wellbeing, but the types of neighbourhoods we live in, and the opportunities they afford us. This consensus is reflected by published evidence of inequalities in people's life chances between different places, the Marmot review "Fair Society Healthy Lives" demonstrates by association that neighbourhoods that are more deprived also tend to have populations with lower life expectancies.The overall evidence for neighbourhood effects is somewhat limited, only recently has research begun to demonstrate an unambiguous association between neighbourhood and health. Health outcomes and behaviours studied have particularly focused on diet and physical activity, which have been linked to a range of neighbourhood contextual factors including access to green space, places to buy fresh fruit and vegetables (or conversely fast-food), and the 'walkability' of the built environment. Mental health and wellbeing has received markedly less focus, although some high quality studies have looked at the relationship between neighbourhood green space and wellbeing. Emerging epidemiological evidence has benefitted from researchers working across disciplines, particularly with geographers, in order to produce robust study designs. Nonetheless, evidence of a causal link between our changing neighbourhoods, and our changing health is rather less forthcoming, owing to the complexity of the social-ecological processes hypothesised to be taking place.This research project aims to make a significant contribution to empirical evidence for neighbourhood effects by applying 'systems thinking.' This means treating neighbourhoods as complex, dynamic multi-factorial processes, where existing research treats them as simple, stationary snapshots with limited depth. Specific training in 'Urban Informatics' will greatly augment my existing expertise in Geographic Information Science and Social Epidemiology, and give me the tools required to operationalise dynamic, rather than static, representations of neighbourhoods. Capturing how neighbourhoods change over time alongside how individuals change over time is fundamental to unpicking causality.In particular, the research will focus on the effects on mental health and wellbeing of people moving, expecting to move, or preferring to move, at both the neighbourhood scale, and the individual scale. We know that neighbourhoods are not equal, and yet in terms of population movement - people moving into or out of a neighbourhood - we tend to treat them equally. Studies have suggested that failing to account for this dynamic neighbourhood process - the residential movement of people - can have implications on our expectations for people's health through what are termed 'selection effects'. These neighbourhood compositional factors will be studied alongside contextual aspects of neighbourhoods, notably neighbourhood crime and disorder and neighbourhood economic insecurity, in order to explain how mental health and wellbeing is affected by neighbourhood social and environmental changes.This type of research is only just becoming possible, due to a range of complementary factors. Firstly, world-class training at New York University's Centre for Urban Science and Progress (NYU CUSP). Secondly, the availability of the UK's rich longitudinal "Understanding Society" survey data. Thirdly, the proliferation of open, and free government administrative data, and finally, the growing support from research councils in promoting 'Big Data' and informatics approaches. This fellowship takes advantage of all of these factors, with the hope of producing world-leading research on neighbourhoods and health.

人们越来越认识到，影响健康和福祉的不仅仅是个人特征和行为，还有我们居住的社区类型以及它们为我们提供的机会。这一共识反映在不同地方之间人们生活机会不平等的已发表证据中，土拨鼠的评论“公平社会健康生活”通过关联表明，贫困程度较高的社区往往拥有较低预期寿命的人口。影响有些有限，直到最近研究才开始证明邻里关系与健康之间存在明确的关联。研究的健康结果和行为特别关注饮食和身体活动，这与一系列邻里环境因素有关，包括绿地的可达性、购买新鲜水果和蔬菜的地方（或相反的快餐）以及“步行便利性”。 ”的建筑环境。尽管一些高质量的研究探讨了社区绿地与幸福感之间的关系，但心理健康和幸福感受到的关注明显较少。新出现的流行病学证据受益于跨学科研究人员的工作，特别是与地理学家的合作，以产生稳健的研究设计。尽管如此，由于假设正在发生的社会生态过程的复杂性，我们不断变化的社区与不断变化的健康之间存在因果关系的证据却很少出现。该研究项目旨在为以下方面的经验证据做出重大贡献：通过应用“系统思维”来改善邻里效应。这意味着将社区视为复杂、动态的多因素过程，而现有研究将其视为深度有限的简单、固定的快照。 “城市信息学”的具体培训将极大地增强我在地理信息科学和社会流行病学方面现有的专业知识，并为我提供操作动态而非静态的社区表示所需的工具。捕捉社区如何随时间变化以及个人如何随时间变化对于解开因果关系至关重要。特别是，该研究将重点关注在社区中搬迁、期望搬迁或倾向于搬迁的人们对心理健康和福祉的影响规模和个人规模。我们知道社区并不平等，但就人口流动（人们进出社区）而言，我们倾向于平等对待他们。研究表明，如果不考虑这种动态的邻里过程——人们的居住流动——可能会通过所谓的“选择效应”影响我们对人们健康的期望。这些社区构成因素将与社区的背景因素一起研究，特别是社区犯罪和骚乱以及社区经济不安全，以解释心理健康和福祉如何受到社区社会和环境变化的影响。此类研究才刚刚成为可能，由于一系列互补因素。首先，纽约大学城市科学与进步中心（NYU CUSP）提供世界一流的培训。其次，英国丰富的纵向“理解社会”调查数据的可用性。第三，开放、免费的政府行政数据的激增，最后，研究委员会对推广“大数据”和信息学方法的支持不断增加。该奖学金利用所有这些因素，希望能够产生世界领先的社区和健康研究。